A preoperative nomogram incorporating semantic MRI features for predicting breast cancer recurrence: a propensity...

Accurate preoperative assessment for postoperative recurrence risk remains challenging in breast cancer (BC). This study aimed to develop and validate a nomogram based on routinely available preoperative semantic MRI features for predicting postoperative recurrence in BC, using propensity score matching (PSM) to reduce confounding.

A retrospective analysis was conducted in 395 patients with primary BC who underwent curative surgery and preoperative breast MRI between January 2010 and December 2023. MRI features were independently assessed by two radiologists blinded to clinical outcomes. PSM was applied to balance age, family history of BC, and type of surgery. After PSM (1:2 ratio), 204 patients (68 with recurrence and 136 without) were included for analysis. Univariate logistic regression identified candidate predictors (P < 0.05). Given the relatively low events-per-variable (EPV = 4.9, 68 events/14 candidate predictors), the Enter method was utilized for multivariate logistic regression based on clinical relevance to minimize the risk of overfitting. A nomogram was constructed and internally validated using bootstrap resampling (1000 iterations) with optimism correction.

Multivariate analysis identified three predictors of recurrence: adjacent vessel sign, tumor size >5 cm, and axillary lymph node metastasis. The nomogram demonstrated good discrimination, with areas under the receiver operating characteristic curve of 0.809 in the training cohort and 0.797 in the validation cohort. Calibration was satisfactory (slope = 1.000, intercept = -0.000), with mean absolute errors of 0.087 in the training cohort and 0.070 in the validation cohort. Decision curve analysis confirmed clinical utility across threshold probabilities of approximately 10% to 70%.

The preoperative MRI-based nomogram demonstrated good predictive performance and satisfactory calibration in this internally validated cohort, suggesting its potential as a tool for individualized risk stratification prior to treatment. However, as this was a single-center study with internal validation only, external validation in independent multicenter cohorts is required before clinical implementation.